Deep learning to improve the sensitivity of Di-Higgs searches in the 4b channel

Autor: Cheng-Wei Chiang, Feng-Yang Hsieh, Shih-Chieh Hsu, Ian Low

Publikováno v: Journal of High Energy Physics, Vol 2024, Iss 9, Pp 1-25 (2024)

Abstract The study of di-Higgs events, both resonant and non-resonant, plays a crucial role in understanding the fundamental interactions of the Higgs boson. In this work we consider di-Higgs events decaying into four b-quarks and propose to improve

Externí odkaz: https://doaj.org/article/8741df2a72a64be6a5fe8512cd71d81f

Reconstruction of unstable heavy particles using deep symmetry-preserving attention networks

Autor: Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi

Publikováno v: Communications Physics, Vol 7, Iss 1, Pp 1-11 (2024)

Abstract Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. An approach based on a generalized attention me

Externí odkaz: https://doaj.org/article/0352c91a13ad41b08b551563f0c068b9

Search for dark photons with the FASER detector at the LHC

Autor: Henso Abreu, John Anders, Claire Antel, Akitaka Ariga, Tomoko Ariga, Jeremy Atkinson, Florian U. Bernlochner, Tobias Boeckh, Jamie Boyd, Lydia Brenner, Franck Cadoux, David W. Casper, Charlotte Cavanagh, Xin Chen, Andrea Coccaro, Monica D'Onofrio, Ansh Desai, Sergey Dmitrievsky, Candan Dozen, Yannick Favre, Deion Fellers, Jonathan L. Feng, Carlo Alberto Fenoglio, Didier Ferrere, Iftah Galon, Stephen Gibson, Sergio Gonzalez-Sevilla, Yuri Gornushkin, Carl Gwilliam, Daiki Hayakawa, Shih-Chieh Hsu, Zhen Hu, Giuseppe Iacobucci, Tomohiro Inada, Sune Jakobsen, Hans Joos, Enrique Kajomovitz, Hiroaki Kawahara, Alex Keyken, Felix Kling, Daniela Köck, Umut Kose, Rafaella Kotitsa, Susanne Kuehn, Helena Lefebvre, Lorne Levinson, Ke Li, Jinfeng Liu, Jack MacDonald, Chiara Magliocca, Fulvio Martinelli, Josh McFayden, Sam Meehan, Matteo Milanesio, Théo Moretti, Magdalena Munker, Mitsuhiro Nakamura, Toshiyuki Nakano, Friedemann Neuhaus, Laurie Nevay, Ken Ohashi, Hidetoshi Otono, Hao Pang, Lorenzo Paolozzi, Brian Petersen, Markus Prim, Michaela Queitsch-Maitland, Hiroki Rokujo, Elisa Ruiz-Choliz, Jorge Sabater-Iglesias, Jakob Salfeld-Nebgen, Osamu Sato, Paola Scampoli, Kristof Schmieden, Matthias Schott, Anna Sfyrla, Savannah Shively, Yosuke Takubo, Noshin Tarannum, Ondrej Theiner, Eric Torrence, Sebastian Trojanowski, Svetlana Vasina, Benedikt Vormwald, Di Wang, Eli Welch, Samuel Zahorec, Stefano Zambito

Publikováno v: Physics Letters B, Vol 848, Iss , Pp 138378- (2024)

The FASER experiment at the LHC is designed to search for light, weakly-interacting particles produced in proton-proton collisions at the ATLAS interaction point that travel in the far-forward direction. The first results from a search for dark photo

Externí odkaz: https://doaj.org/article/b82c52a1aff34e43997da84f80f761ef

Learning to identify semi-visible jets

Autor: Taylor Faucett, Shih-Chieh Hsu, Daniel Whiteson

Publikováno v: Journal of High Energy Physics, Vol 2022, Iss 12, Pp 1-26 (2022)

Abstract We train a network to identify jets with fractional dark decay (semi-visible jets) using the pattern of their low-level jet constituents, and explore the nature of the information used by the network by mapping it to a space of jet substruct

Externí odkaz: https://doaj.org/article/9a9aad72ed6f44568c16218f37041f17

Performance of a geometric deep learning pipeline for HL-LHC particle tracking

Autor: Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steven Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alex Ballow, Alina Lazar

Publikováno v: European Physical Journal C: Particles and Fields, Vol 81, Iss 10, Pp 1-14 (2021)

Abstract The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. T

Externí odkaz: https://doaj.org/article/3869b806bd4f475fb2adf7edf5616d69